Describes open challenges and ongoing work for mapping the human functional connectome and identifying inter-individual variation in the connectome that maps to phenotype and clinical outcomes. Also describes open science initiatives to help scientists from disparate backgrounds to become involved in this research.
Nightside clouds and disequilibrium chemistry on the hot Jupiter WASP-43b
Computational approaches for mapping the human connectome
1. Computational approaches for
mapping the human connectome
R. Cameron Craddock, PhD
Director, Computational Neuroimaging Lab
Nathan S. Kline Institute for Psychiatric Research
Director of Imaging, Center for the Developing Brain
Child Mind Institute
March 30, 2016
2. Functional Magnetic Resonance
Imaging (fMRI)
An fMRI time-course is formed by the rapid acquisition of MR
images which are sensitive to the “blood oxygen level dependent”
(BOLD) contrast
1. Hemoglobin, the protein which transports oxygen in blood, contains four
heme molecules, each with an atom of iron
2. Deoxy-hemoglobin is paramagnetic and creates a magnetic gradient that
dephases the MRI signal
3. Oxy-hemoglobin is diamagnetic and does not affect the MRI signal
3. Hemodynamic Response
1. Initially neuronal oxygen consumption increases the amount
of deoxy-hemoglobin and the MR signal decreases
2. Blood flow increases, bringing more oxygenated blood to the
area than is required, resulting in a signal increase
3. When neuronal activity ceases, the signal returns to baseline
after a brief undershoot
5. Resting State Functional Connectivity
Biswal et al. MRM 1995
Intrinsic activity is ‘‘ongoing neural and metabolic
activity which is not directly associated with
subjects’ performance of a task’’-Raichle TICS 2010
7. Functional Connectivity Analysis
1. Data are preprocessed
2. Individual level FC maps are generated by ROI correlation with rest
of brain, ICA, cross-correlation between several ROIs, or other
method
3. FC maps are compared between groups feature-by-feature (voxel-
by-voxel) using t-tests
8. Data Driven ROI Atlas
Craddock et al. Human Brain Mapping 2012.
10. The Human Connectome
• The sum total of all of the brain’s
connections
– Structural connections:
synapses and fibers
• Diffusion MRI
– Functional connections:
synchronized physiological
activity
• Resting state functional
MRI
• Nodes are brain areas
• Edges are connections
Craddock et al. Nature Methods, 2013.
11. Discovery science of human brain function
1. Characterizing inter-individual variation in connectomes
(Kelly et al. 2012)
2. Identifying biomarkers of disease state, severity, and
prognosis (Craddock 2009)
3. Re-defining mental health in terms of
neurophenotypes, e.g. RDOC (Castellanos 2013)
12. Diagnosing Depression
• SVC successfully learned patterns of functional connectivity
capable of predicting MDD from HC
– Uncovered differences not discovered by t-test analysis
• Feature selection substantially improved the prediction
accuracy of SVC
– Methods that incorporate reliability performed the best
• Method requires selecting and localizing ROIs
– Problematic if there is no previous research, introduces experimenter
bias/error
13. Predicting Intrinsic Brain Activity
Multivariate model of brain activity
xn = b0 + bv
v¹n
å xv +x
Underdetermined problem: solved using support vector
regression or other regularized regression / dimensionality
reduction method
Craddock et al. NeuroImage 2013.
14. Nonparametric prediction, activation,
influence and reproducibility resampling
Predicted Time Course
Observed Time Course
Features
Dataset 1
Observed Time Course
Features
Dataset 2
Model
Estimation
Model
Estimation
wixi+b
i
Prediction
Prediction Accuracy
Reproducibility
Prediction
wixi+b
i
Predicted Time Course
Prediction Accuracy
Network
Model
Network
Model
B
A
15. Prediction Accuracy
• Measure of the generalization ability of a model
• Can be interpreted as a measure of the information
content in the model about the region being
modeled
p(xn x1...xv ) » I(xn x1...xv )
20. Inter-subject prediction
• 480 subjects
– 69 DZ twin pairs
– 80 MZ twin pairs
– 200 Non-siblings
• Train on one individual, test with another
– Intra individual
– Between siblings (MZ, DZ)
– Age and sex matched non-siblings
29. Exp. Design
Class Training
Labels
Training run
Time-Labeled
Scans
Image Recon and SVM
Classification
Image DataData Acquisition
Stimulus Presentation
Stimulus
Conventional FMRI
Test Data Classifier Output
Testing Run
Real-Time Tracking RSNs
LaConte, et al. (2007) Hum Brain Mapp. 28: 1033-1044
Stephen LaConte August 19, 2009
30. Stimulus seen by volunteer
Updated fMRI
results Motion tracking and correction
Intensity (brightness) of a single voxel, changing
during stimulus conditions
Controller interface for display parameters
31. RT Neurofeedback of DMN
• Test hypothesis of DMN dysregulation in
depression, ADHD, aging, etc …
32. Preprocessing
Skullstripping (3dSkullStrip)
Linear Registration to MNI (flirt)
Segmentation (fast)
Anatomical Acquisition
(T1 MPRAGE 4m 30s)
1 - 2.5min
12s
30s
Coregister EPI to T1 (flirt+BBR)
Write DMN template into EPI space
Write WM+CSF mask into EPI space
30s
1s
1s
Mask Acquisition
(EPI 4m 30s)
Calculate mean (3dTstat)
Calculate mask (3dAutomask)
1s
1s Resting State (Training) Scan
(EPI 6m)
Motion correction to mean EPI (3dvolreg)
Nuisance variable regression (3dDetrend)
Spatial smoothing (3dmerge)
Spatial regression to extract DMN time course (fsl_glm)
Support vector regression training (3dsvm)
13s
2s
2s
32s
6-20s
Indicates data dependency
• Online preprocessing can be performed in ~ 5 minutes,
most of which can occur in parallel with acquisition
33. Online Denoising
• fMRI activity is confounded by intensity modulations induced by
head motion, physiological noise, scanner drift, …
• Implemented RT denoising in AFNI to remove contributions of
confounds
– Nth order polynomial
– Global mean
– Mask average time series (i.e. WM, CSF)
– Motion parameters (6 or 24 regressor models)
– Spatial smoothing
• Adds ~ 5 ms of delay
36. Results
0.00.10.20.30.40.50.6
3 1 7 13 6 9 5 10 11 8 4 2 12
Subject
Accuracy
Feedback
No feedback
FB NOFB
0.10.20.30.40.50.6
1 2 1 2
Scan Number
Accuracy
p = 0.055p = 0.68
Accuracy was measured from Pearson’s correlation between task
paradigm and DMN activity extracted after post-processing.
37. Behavioral Correlates
Measures that were significantly associated with DN regulation include (p<0.05,
FDR corrected): the affect intensity measure (AIM), ruminative responses scale
(RRS), and the imaginal processes inventory.
38. Enhanced and suppressed craving:
Classification in real time
PEER, LaConte S.M. et al. ISMRM 2006, Sathian K et al. NeuroImage 2011.
39. Principles of Open Neuroscience
Data, tools and ideas should be openly shared
-The Neuro Bureau Manifesto
http://www.neurobureau.org
41. C-PAC
• Pipeline to automate preprocessing
and analysis of large-scale datasets
• Configurable to enable “plurality” –
evaluate different processing
parameters and strategies
• Automatically identifies and takes
advantage of parallelism on multi-
threaded, multi-core, and cluster
architectures
• “Warm restarts” – only re-compute
what has changed
• Open science – open source
44. Quality Assessment Protocol
• Spatial Measures
– Contrast to Noise Ratio
– Entropy Focus Criterion
– Foreground to Background
Energy Ratio
– Smoothness (FWHM)
– % Artifact Voxels
– Signal-to-Noise Ratio
• Temporal Measures
– Standardized DVARS
– Median distance index
– Mean Functional
Displacement
– # Voxels with FD > 0.2m
– % Voxels with FD > 0.2m
http://preprocessed-connectomes-project.github.io/quality-assessment-protocol/
45. Quality Assessment Protocol (2)
• Implemented in python
• Normative datasets to help
learn thresholds for quality
control
– ABIDE
– CoRR
http://preprocessed-connectomes-project.github.io/quality-assessment-protocol/
49. Acknowledgments
• CMI/NKI
– Michael Milham, MD, PHD
– Zarrar Shehzad
– Stan Colcombe, PhD
• Virginia Tech Carilion Research Institute
– Stephen LaConte, PhD
– Jonathan Lisinski, MS
• Siemens Medical
– Keith Heberlein, PhD
– Chris Glielmi, PhD
• Research Funded in part by a NARSAD Young Investigator
Award and NIMH R01MH101555
Thank
You!